We address two class-imbalance scenarios within the FSL framework: 1) imbalanced support set, and 2) imbalanced meta-training dataset. For the imbalanced support set scenario, we first focus on the very low-data range with an average support set size of 25 samples (5 avr. shot). We train FSL models using Standard (episodic) meta-training [1]} using 5-shot 5-way tasks, as well as Random-Shot meta-training [2]}, [3]}, [4]} using 1-9shot 5-way random-distribution tasks (as described in Section REF ). We pre-train baselines (i.e., Fine-Tune, 1-NN, Baseline++) using mini-batch gradient descent, and then fine-tune on the support or perform a 1-NN classification. We evaluate all baselines and models using a wide range of imbalanced meta-testing tasks. In contrast to previous work, we evaluate models using two additional imbalance distributions, linear and step; this allows us to control the imbalance level deterministically and provide insights from multiple angles. For the imbalanced meta-dataset scenario, we vary the class distributions of the meta-training datasets. We isolate this level of imbalance by meta-training and meta-evaluating on balanced FSL tasks. All main experiments are repeated three times with different initialization seeds. Each data point represents the average performance of over 600 meta-testing tasks per run.
